Department of Information, Daping Hospital of Army Medical University, 10 Changjiang Access Road, Chongqing, 400042, China.
BMC Med Inform Decis Mak. 2020 Apr 15;20(1):67. doi: 10.1186/s12911-020-1085-4.
The International Classification of Diseases, 10th Revision (ICD-10) has been widely used to describe the diagnosis information of patients. Automatic ICD-10 coding is important because manually assigning codes is expensive, time consuming and error prone. Although numerous approaches have been developed to explore automatic coding, few of them have been applied in practice. Our aim is to construct a practical, automatic ICD-10 coding machine to improve coding efficiency and quality in daily work.
In this study, we propose the use of regular expressions (regexps) to establish a correspondence between diagnosis codes and diagnosis descriptions in outpatient settings and at admission and discharge. The description models of the regexps were embedded in our upgraded coding system, which queries a diagnosis description and assigns a unique diagnosis code. Like most studies, the precision (P), recall (R), F-measure (F) and overall accuracy (A) were used to evaluate the system performance. Our study had two stages. The datasets were obtained from the diagnosis information on the homepage of the discharge medical record. The testing sets were from October 1, 2017 to April 30, 2018 and from July 1, 2018 to January 31, 2019.
The values of P were 89.27 and 88.38% in the first testing phase and the second testing phase, respectively, which demonstrate high precision. The automatic ICD-10 coding system completed more than 160,000 codes in 16 months, which reduced the workload of the coders. In addition, a comparison between the amount of time needed for manual coding and automatic coding indicated the effectiveness of the system-the time needed for automatic coding takes nearly 100 times less than manual coding.
Our automatic coding system is well suited for the coding task. Further studies are warranted to perfect the description models of the regexps and to develop synthetic approaches to improve system performance.
国际疾病分类第 10 版（ICD-10）已广泛用于描述患者的诊断信息。自动 ICD-10 编码非常重要，因为手动分配代码既昂贵、耗时又容易出错。尽管已经开发了许多方法来探索自动编码，但很少有方法在实践中得到应用。我们的目标是构建一个实用的自动 ICD-10 编码机，以提高日常工作中的编码效率和质量。
在这项研究中，我们提出使用正则表达式（regexp）在门诊和入院及出院时建立诊断代码与诊断描述之间的对应关系。regexp 的描述模型被嵌入到我们升级的编码系统中，该系统查询诊断描述并分配唯一的诊断代码。与大多数研究一样，我们使用精度（P）、召回率（R）、F 度量（F）和整体准确性（A）来评估系统性能。我们的研究有两个阶段。数据集是从出院病历主页上的诊断信息中获得的。测试集分别来自 2017 年 10 月 1 日至 2018 年 4 月 30 日和 2018 年 7 月 1 日至 2019 年 1 月 31 日。
在第一测试阶段和第二测试阶段，P 值分别为 89.27%和 88.38%，这表明精度很高。自动 ICD-10 编码系统在 16 个月内完成了超过 160000 个代码，减轻了编码员的工作量。此外，手动编码和自动编码所需时间的比较表明了该系统的有效性——自动编码所需的时间不到手动编码的 100 倍。
我们的自动编码系统非常适合编码任务。需要进一步的研究来完善 regexp 的描述模型，并开发综合方法来提高系统性能。
